Sensitivity and Accuracy in Processing X-ray Spectrum Images

A third-generation silicon drift detector (SDD) in the form of a silicon multicathode detector (SMCD) was tested as an analytical x-ray spectrometer on a scanning electron microscope. Resolution, output count rate, and spectral quality were examined as a function of the detector peaking time from 8 micros to 250 ns and over a range of input count rate (dead time). The SDD-SMCD (50 mm2 active area) produced a resolution of 134 eV with a peaking time of 8 micros. The peak width and peak channel were nearly independent of the input count rate (at 8 micros peaking time, the peak width degradation was 0.003 eV/percent dead time and peak position change was -0.7 eV over the dead time range tested). Maximum output count rates as high as 280 kHz were obtained with a 500 ns peaking time (188 eV resolution) and 500 kHz with a 250 ns peaking time (217 eV resolution). X-ray spectrum imaging was achieved with a pixel dwell time as short as 10 ms (with 1.3 ms overhead) in which a 2048 channel (10 eV/channel) spectrum with 2-byte intensity range was recorded at each pixel (scanned at 128 x 128). With a 220 kHz output count rate, a minor constituent of iron (present at a concentration of 0.04 mass fraction or 4 weight %) in an aluminum-nickel alloy could be readily detected in the x-ray maps derived from the x-ray spectrum image database accumulated in 185 s.

Energy-dispersive X-ray (EDX) spectrum imaging, where a full spectrum is acquired at each pixel in a rastered image, combines the advantages of imaging and analysis for quantitative compositional mapping. This method has great advantages for the analysis of compositional variations, because the contrast inherent in EDX spectrum images arises from the distinctiveness of the characteristic X-ray spectra in various regions of the surveyed microstructure. EDX spectrum imaging provides for simultaneous spatial, spectral and temporal resolution of the X-rays emitted from the specimen. Many EDX manufacturers have incorporated a spectrum imaging capability within their acquisition and analysis software, and ongoing improvements in computing power will enhance the practicality of spectrum imaging methods, in particular the acquisition, manipulation and analysis of the large raw data files that are generated with this method. This paper surveys the potential role of spectrum imaging in tasks that support true quantitative X-ray microanalysis of bulk specimens: robust qualitative analysis, the construction of representative spectra that describe the specimen, and the extraction of elemental concentrations from the representative spectra.

Spectrum imaging, where a complete spectrum is acquired at each pixel in an image, is potentially a very powerful technique for the characterization of materials. The primary challenge to overcome is to extract all of the relevant information from what are typically large data sets that cannot be readily visualized in their entirety. For energy-dispersive X-ray (EDX) spectrum images, simple mapping has typically been done. In this work, the application of multivariate statistical analysis (MSA) to the characterization of a metal/alumina braze by SEM-EDX spectrum images is described. The braze characterized in this work joins polycrystalline alumina and a copper-silver eutectic alloy containing some titanium (an ‘active’ metal). The specimen geometry consisted of a sandwich of two pieces of alumina, two braze layers and a Kovar (primarily Fe, Ni and Co) filler layer in the middle. The entire assembly was heat-treated to bond the interfaces and then a polished cross-section was prepared.

Multi-energy computed tomography (MECT) has a great potential to enable many novel clinical applications such as simultaneous multi-contrast imaging. The purpose of this study was to implement triple-beam MECT on a traditional energy-integrating-detector (EID) CT platform (EID-MECT). This was accomplished by mounting a z-axis split-filter (0.05mm Au, 0.6mm Sn) on Tube A of a dual-source EID CT scanner. With the two split x-ray beams from Tube A and the third beam from Tube B, three beams with different x-ray spectra can be simultaneously acquired. With Tube B operated at 70 or 80kV and Tube A at 120 or 140kV, four different triple-beam configurations were calibrated for MECT measurements: 70/Au120/Sn120, 80/Au120/Sn120, 70/Au140/Sn140, and 80/Au140/Sn140 kV. Iodine (I), gadolinium (Gd), bismuth (Bi) samples, and their mixtures were prepared for 2 three-material-decomposition tasks and 1 four-material-decomposition task. For each task, samples were placed in a water phantom and scanned using each of the four triple-beam configurations. For comparison, the same phantom was also scanned using three other dual-energy CT (DECT) or MECT technologies: twin-beam DECT (TB-DECT), dual-source DECT (DS-DECT), and photon-counting-detector CT (PCD-CT), all with optimal x-ray spectrum settings and at equal volume CT dose index (CTDIvol). The phantom for four-material decomposition (I/Gd/Bi/Water imaging) was scanned using the PCD-CT only (140kV with 25, 50, 75, and 90keV). Image-based material decomposition was performed to acquire material-specific images, on which the mean basis material concentrations and noise levels were measured and compared across all triple-beam configurations in EID-MECT and various DECT/MECT systems. The optimal triple-beam configuration was task-dependent with 70/Au120/Sn120, 70/Au140/Sn140, and 70/Au120/Sn120kV for I/Gd/Water, I/Bi/Water, and I/Gd/Bi/Water material decomposition tasks, respectively. At equal radiation dose level, EID-MECT provided comparable or better quantification accuracy in material-specific images for all three material decomposition tasks, compared to EID-based DECT and PCD-CT systems. In terms of noise level comparison, EID-MECT-derived material-specific images showed lower noise levels than TB-DECT and DS-DECT, but slightly higher than that from PCD-CT in I/Gd/Water imaging. For I/Bi/Water imaging, EID-MECT showed a comparable noise level to DS-DECT, and a much lower noise level than TB-DECT and PCD-CT in all material-specific images. For the four-material decomposition task involving I/Gd/Bi/Water, the bismuth-specific image derived from EID-MECT was slightly noisier, but both iodine- and gadolinium-specific images showed much lower noise levels in comparison to PCD-CT. For the first time, an EID-based MECT system that can simultaneously acquire three x-ray spectra measurements was implemented on a clinical scanner, which demonstrated comparable or better imaging performance than existing DECT and MECT systems.

When will a small or low-contrast feature, such as an embedded second-phase particle, be visible in a scanning transmission electron microscopy (STEM) X-ray map? This work illustrates a computationally inexpensive method to simulate X-ray maps and spectrum images (SIs), based upon the equations of X-ray generation and detection. To particularize the general procedure, an example of nanostructured ferritic alloy (NFA) containing nm-sized Y2Ti2O7 embedded precipitates in ferritic stainless steel matrix is chosen. The proposed model produces physically appearing simulated SI data sets, which can either be reduced to X-ray dot maps or analyzed via multivariate statistical analysis. Comparison to NFA X-ray maps acquired using three different STEM instruments match the generated simulations quite well, despite the large number of simplifying assumptions used. A figure of merit of electron dose multiplied by X-ray collection solid angle is proposed to compare feature detectability from one data set (simulated or experimental) to another. The proposed method can scope experiments that are feasible under specific analysis conditions on a given microscope. Future applications, such as spallation proton-neutron irradiations, core-shell nanoparticles, or dopants in polycrystalline photovoltaic solar cells, are proposed.

PdAu nanocrystals are synthesised by Geobacter sulfurreducens, a dissimilatory metal-reducing bacterium, and the resulting bimetallic nanocrystal-decorated microbes are imaged using a range of advanced electron microscopy techniques. Specifically, the first example of elemental mapping of fully hydrated biological nanostructures using scanning transmission electron microscope (STEM) energy dispersive X-ray (EDX) spectrum imaging within an environmental liquid-cell is reported. These results are combined with cryo-TEM and ex situ STEM imaging and EDX analysis with the aim of better understanding microbial synthesis of bimetallic nanoparticles. It is demonstrated that although Au and Pd are colocalized across the cells, the population of nanoparticles produced is bimodal, containing ultrasmall alloyed nanocrystals with diameters 200 nm in diameter) which show higher Pd contents and exhibit a Pd enriched shell only a few nanometers thick. The application of high-resolution imaging techniques described here offers the potential to visualize the microbe-metal interface during the bioproduction of a range of functional materials by microbial “green” synthesis routes, and also key interfaces underpinning globally relevant environmental processes (e.g., metal cycling).

Elemental mapping at the microstructural level by scanning electron microscopy (SEM) with energy dispersive X-ray spectrometry (EDS), while widely applied in science, engineering, and technology, has been limited in performance by the throughput of the lithium-drifted silicon detector [Si(Li)-EDS] which restricts the number of X-ray counts measured per image pixel. The emergence of the silicon drift detector (SDD-EDS) has greatly extended the X-ray throughput, by a factor of 25 to 70 for the same spectral resolution. This improved performance enables practical X-ray spectrum imaging (XSI), in which a complete X-ray spectrum is recorded at each image pixel. By performing complete quantitative corrections for background, peak overlap, and matrix effects to each pixel spectrum, full compositional mapping can be achieved. Various elemental mapping collection strategies are described, including quantitative mapping at the major (concentration C > 0.1 mass fraction), minor (0.01 ≤ C ≤ 0.1), and trace (C < 0.01) constituent levels, extreme pixel density (gigapixel) mapping, rapid mapping (in 10 seconds or less), and high spatial resolution mapping with the thermal field emission gun scanning electron microscope.

We report results from a 50 ks XMM-Newton observation of the dust-reddened broad-line quasar FTM 0830+3759 (z=0.413) selected from the FIRST/2MASS Red Quasar survey. For this AGN, a very short 9 ks Chandra exposure had suggested a feature-rich X-ray spectrum and HST images revealed a very disturbed host galaxy morphology. Contrary to classical, optically-selected quasars, the X-ray properties of red (i.e. with J-Ks> 1.7 and R-Ks> 4) broad line quasars are still quite unexplored, although there is a growing consensus that, due to moderate obscuration, these objects can offer a unique view of spectral components typically swamped by the AGN light in normal, blue quasars. The XMM-Newton observation discussed here has definitely confirmed the complexity of the X-ray spectrum revealing the presence of a cold (or mildly-ionized) absorber with Nh ~10^{22} cm^-2 along the line of sight to the nucleus and a Compton reflection component accompanied by an intense Fe K emission line in this quasar with a Lum(2-10) ~5 x 10^{44} erg/s. A soft-excess component is also required by the data. The match between the column density derived by our spectral analysis and that expected on the basis of reddening due to the dust suggests the possibility that both absorptions occur in the same medium. FTM 0830+3759 is characterized by an extinction/absorption-corrected X-ray-to-optical flux ratio alphaox = -2.3, that is steeper than expected on the basis of its UV luminosity. These findings indicate that the X-ray properties of FTM 0830+3759 differs from those typically observed for optically-selected broad line quasars with comparable hard X-ray luminosity.

Accurate low-dose CT simulation is required to efficiently assess reconstruction and dose reduction techniques. Projection domain noise insertion requires proprietary information from manufacturers. Analytic image domain noise insertion methods are successful for linear reconstruction algorithms, however extending them to non-linear algorithms remains challenging. Emerging, deep-learning-based image domain noise insertion methods have potential, but few approaches have explicitly incorporated physics information and a texture-synthesis model to guide the generation of locally and globally correlated noise texture. We proposed a physics-informed model-based generative neural network for simulating scanner- and algorithm-specific low-dose CT exams (PALETTE). It is expected to provide an alternative to projection domain noise insertion methods in the absence of manufacturers' proprietary information and tools. PALETTE integrated a physics-based noise prior generation process, a Noise2Noisier sub-network, and a noise texture synthesis sub-network. The Noise2Noisier sub-network provided a bias prior, which, combined with the noise prior, served as the inputs to noise texture synthesis sub-network. Explicit regularizations in spatial and frequency domains were developed to account for noise spatial correlation and frequency characteristics. For proof-of-concept, PALETTE was trained and validated for a commercial iterative reconstruction algorithm (SAFIRE, Siemens Healthineers), using the paired routine and 25% dose images from CT phantoms (lateral size 30-40 cm; three training and four testing phantoms) and open-access patient cases (10 training and 20 testing cases). In phantom validation, noise power spectra (NPS) were compared in water background and tissue-mimicking inserts, using peak frequency and mean-absolute-error (MAE). In patient case evaluation, visual inspection and quantitative assessment were conducted on axial, coronal, and sagittal planes. Local and global noise texture were visually inspected in low-dose CT images and the difference images between routine and low dose. Noise levels in liver and fat were measured. Local and global 2D Fourier magnitude spectra of the difference images and the corresponding radial mean profiles were used to assess similarity in noise frequency components within tissues and entire field-of-view, using spectral correlation mapper (SCM) and spectral angle mapper (SAM). Several baseline neural network models (e.g., GAN) were included in the evaluation. Statistical significance was tested using a t-test for related samples. PALETTE-derived NPS showed accurate noise peak frequency (PALETTE/reference: water 1.40/1.40 lp/cm; inserts 1.7/1.7lp/cm) and small MAE (≤0.65 HU2cm2). PALETTE created anatomy-dependent noise texture, showing realistic local and global granularity and streaks. No statistically significant difference was observed in noise levels (p > 0.05). Noise range was comparable across 3D image volume (PALETTE / reference):liver - [18.0, 53.4] / [19.3, 50.0] HU; fat - [11.7, 42.4] / [12.1, 41.3] HU. Percent absolute difference of local noise was small (mean ± standard deviation): liver 4.1%±3.1%, fat 4.6%±3.1%. Noise frequency distribution was close to the reference (mean per case): SCM ≥ 0.92, SAM ≤ 0.22. Additionally, PALETTE outperformed all baseline models in visual inspection and quantitative comparison. PALETTE can provide high-quality image domain noise insertion for simulating accurate low-dose CT images created with a commercial non-linear reconstruction algorithm.

Extended abstract of a paper presented at Microscopy and Microanalysis 2008 in Albuquerque, New Mexico, USA, August 3 – August 7, 2008

IntroductionArterial calcification can limit the visualisation of vessel lumen at CT coronary angiography (CTCA). Dual energy CT (DECT) using two x-ray spectra of differing energy, either from distinct sources or...

Energy dispersive x-ray (EDX) spectrum imaging yields compositional information with a spatial resolution down to the atomic level. However, experimental limitations often produce extremely sparse and noisy EDX spectra. Under such conditions, every detected x-ray must be leveraged to obtain the maximum possible amount of information about the sample. To this end, we introduce a robust multiscale Bayesian approach that accounts for the Poisson statistics in the EDX data and leverages their underlying spatial correlations. This is combined with EDX spectral simulation (elemental contributions and Bremsstrahlung background) into a Bayesian estimation strategy. When tested using simulated datasets, the chemical maps obtained with this approach are more accurate and preserve a higher spatial resolution than those obtained by standard methods. These properties translate to experimental datasets, where the method enhances the atomic resolution chemical maps of a canonical tetragonal ferroelectric PbTiO3 sample, such that ferroelectric domains are mapped with unit-cell resolution.

Snapshot microlens array microscopic hyperspectral imaging systems do not require a scanning process and obtain (x,y,λ) three-dimensional data cubes in one shot. Currently, the three-dimensional spectra image data are interleaved on a charge-coupled device detector, which increases subsequent data processing difficulty. The optical design software cannot simulate actual engineering installation and adjustment results accurately and the tracking results cannot guide precise rapid online calibration of the snapshot microlens array microscopic hyperspectral imaging system. To solve these problems, we propose an accurate spectral image reconstruction model based on optical tracing, derive spatial dispersion equations for the prisms and gratings, establish an algorithm model for the correspondence between the microlens array's surface dispersion spectral distribution and its imaging position, and propose a three-dimensional spectral image reconstruction algorithm. Experimental results show that this algorithm's actual spectral calibration error is better than 0.2 nm. This meets the image processing requirements of snapshot microlens array microscopic hyperspectral systems.

X-ray K-Edge Subtraction Computed Tomography (KES-CT) is based on the acquisition of two images at different energies, one below and one above the Kedge of a contrast agent. KES-CT is mainly performed at synchrotron facilities where a tunable monochromatic X-ray beam is available. Thanks to innovative Photon Counting Xray Detectors (PCXDs), it would be desirable to collect the two images in a single shot with a conventional polychromatic Xray spectrum. This approach, sometimes called spectral-CT or color-CT eliminates the risk of misregistration due to motion between consecutive acquisitions and it should allow for scans with much lower doses of contrast medium. Spectral CT is considered very promising but its practical application is being hampered by several practical issues, one of these being the charge sharing affecting the energy resolution of PCXDs. However, latest generations of PCXDs implement hardware solutions to cope with the charge sharing effects, thus allowing sharper color sensitivity. This work presents a K-edge spectral CT imaging preliminary protocol based on the Pixirad-I/PixieIII detector where discrete tomography is used to present the reconstructed slices as color images. Results show that when a solution for the charge sharing issue is considered and refined reconstruction methods are applied, accurate K-edge subtraction imaging with conventional sources can be performed.

As the grating-based phase contrast imaging has drawn much attention in various scientific fields, there is an increasing demand for a compact imaging system with high efficiency in practical applications. In this paper, a compact imaging configuration by replacing the source grating in a conventional imaging system with a robust, easily-fabricated, and cost-effective x-ray polycapillary lens is proposed and its feasibility is examined using a full-scale Monte Carlo simulation. A typical x-ray spectrum for diagnostic mammography is adopted in the simulation. Under this spectrum, a new polycapillary lens is designed and optimized to meet the requirement of the imaging system. The phantom consists of polyethylene, cortical bone, and adipose tissue spheres of different sizes. An accurate phase image of the phantom is successfully reconstructed using an eight-step phase-stepping method. Excellent agreement of the phase shift and attenuation of the phantom is obtained between the simulation and the theoretical prediction. Compared with a conventional imaging system using a source grating at the same parameters, the imaging efficiency of our proposed system is improved 1.7 times, while its background visibility is reduced from 44.4% to 12.4%. Our results suggest the possibility of developing such a bench-top system for flexible phase contrast imaging applications.